Quantization in Machine Learning: 5 Reasons Why It Matters More Than You Think
Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.
Applications with Context Vectors
This post is divided into two parts; they are: • Contextual Keyword Extraction • Contextual Text Summarization Contextual keyword extraction is a technique for identifying the most important words in a document based on their contextual relevance.
Generating and Visualizing Context Vectors in Transformers
This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Different Layers • Visualizing Attention Patterns Unlike traditional word embeddings (such as Word2Vec or GloVe), which assign a fixed vector to each word regardless of context, transformer models generate dynamic representations that depend on surrounding words.
5 Lessons Learned Building RAG Systems
Retrieval augmented generation (RAG) is one of 2025’s hot topics in the AI landscape.
Understanding RAG Part X: RAG Pipelines in Production
Be sure to check out the previous articles in this series: •
Defending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization (SecAlign)
Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications. However, as LLMs have improved, so have the attacks against them. Prompt injection attack is listed as the #1 threat by OWASP to LLM-integrated applications, where an LLM input contains a trusted prompt (instruction) and an untrusted data. The data may contain injected instructions …
Understanding RAG Part IX: Fine-Tuning LLMs for RAG
Be sure to check out the previous articles in this series: •
How to Perform Scikit-learn Hyperparameter Optimization with Optuna
Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that optimizes the model’s performance.
5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs
Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have completely taken over the field of machine learning.
Repurposing Protein Folding Models for Generation with Latent Diffusion
PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models. The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding? In PLAID, we …
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